A Multi-Scale Graph Learning Framework with Temporal Consistency Constraints for Financial Fraud Detection in Transaction Networks under Non-Stationary Conditions

Abstract

Financial fraud detection in transaction networks involves modeling sparse anomalies, dynamic patterns, and severe class imbalance in the presence of temporal drift in the data. In real-world transaction systems, a suspicious transaction is rarely isolated: rather, legitimate and suspicious transactions are often connected through accounts, intermediaries or through temporal transaction sequences. Attribute-based or randomly partitioned learning pipelines are therefore insufficient to detect relationally structured fraud. STC-MixHop, a graph-based framework combining spatial multi-resolution propagation with lightweight temporal consistency modeling for anomaly and fraud detection in dynamic transaction networks. It integrates three components: a MixHop-inspired multi-scale neighborhood diffusion encoder a multi-scale neighborhood diffusion MixHop-based encoder for learning structural patterns; a spatial-temporal attention module coupling current and preceding graph snapshots to stabilize representations; and a temporally informed self-supervised pretraining strategy exploiting unlabeled transaction interactions to improve representation quality. We evaluate the framework primarily on the PaySim dataset under strict chronological splits, supplementing the analysis with Porto Seguro and FEMA data to probe cross-domain component behavior. Results show that STC-MixHop is competitive among graph methods and achieves strong screening-oriented recall under highly imbalanced conditions. The experiments also reveal an important boundary condition: when node attributes are highly informative, tabular baselines remain difficult to outperform. Graph structure contributes most clearly where hidden relational dependencies are operationally important. These findings support a stability-focused view of graph learning for financial fraud detection.

Figures (13)

• Figure 1: Complete architectural diagram of the STC-MixHop framework. The architecture comprises a multi-hop spatial diffusion encoder (MixHop) for capturing extended-range topological context, a temporal attention fusion module for synthesizing information across snapshots, and a two-stage training protocol involving self-supervised contrastive pretraining followed by supervised fine-tuning.
• Figure 2: Complete performance comparison using the PaySim corpus. The bar charts illustrate the performance of STC-MixHop (red) against tabular baselines and other GNN variants across ROC-AUC, PR-AUC, $F_\beta$, and Recall. While tabular methods excel in several ranking metrics, STC-MixHop maintains strong recall characteristics that are valuable for high-recall fraud screening.
• Figure 3: Component ablation study on the PaySim corpus. The results highlight the critical role of multi-resolution spatial diffusion (same-step structure) in maintaining ROC-AUC, while also showing the condition-dependent impact of contrastive learning and temporal attention on the model's overall discriminative power.
• Figure 4: STC-MixHop sensitivity to MixHop diffusion depth $K$ on the PaySim corpus. Increasing $K$ generally improves ROC-AUC and Precision, while Recall peaks at $K=2$, suggesting that moderate topological propagation provides the best balance for identifying elevated-risk nodes.
• Figure 5: STC-MixHop sensitivity to temporal attention dimensionality $d_k$ on the PaySim corpus. The performance metrics exhibit sensitivity to the attention size, with an optimal balance for ROC-AUC and Precision found around $d_k=64$, while Recall reaches its maximum at $d_k=96$.